For the node-sensitive tasks, we report the classification accuracy on the test nodes after 10 runs, which are listed in Table  REF . We compare our results against several strong baselines and state-of-the-art approaches proposed in previous works. In particular, we re-implement a two-layer GAT model[1]} by PyTorch, namely SP-GAT*, with \(F^{\prime }\) , the number of hidden units, equals to 8. For a fair comparison, SP-GAT* accepts the same sparse representations of matrices used in our model.
